Q: What plotting backends does Physt support and are they reliable?

Physt supports matplotlib for static plots, vega (broken), plotly (basic), folium for maps, and ASCII. Matplotlib is the most stable and feature-rich, while others are experimental, so users should verify compatibility for their specific visualization needs.

Q: How do I export Physt histograms to JSON or other formats?

Physt provides I/O capabilities for exporting to JSON, xarray, ROOT files, and pandas DataFrames. Use methods like `hist.to_json()` or similar conversions as mentioned in the features list, though some formats like xarray are experimental and may require additional dependencies.

Q: Is Physt suitable for big data with dask arrays?

Physt has alpha-level support for dask arrays, allowing memory-effective histogramming of large datasets, but this feature is not fully stable. For production big data workflows, consider testing thoroughly or using more mature alternatives like boost-histogram.

Question 1

How do I create a 2D histogram with Physt?

Accepted Answer

Use the `h2` function with two data arrays and optional binning methods like 'pretty'. For example, `h2(iris['sepal_length'], iris['sepal_width'], 'pretty', bin_count=[12, 7])` creates a 2D histogram from the Iris dataset, as shown in the README example with seaborn integration.

Question 2

Physt vs numpy.histogram: which is better for data analysis?

Accepted Answer

Physt is superior for dynamic, updateable histograms with rich metadata and multiple data sources, while numpy.histogram is simpler and faster for basic static binning. Choose Physt if you need manipulation features like adding data post-creation or specialized coordinate systems.

Question 3

Can Physt handle streaming data efficiently?

Accepted Answer

Physt supports updateable histograms with the `<<` operator for incremental data, but adaptive rebinning for unknown data streams is a beta feature. For real-time applications, you may need to rely on basic updates or wait for stable adaptive rebinning improvements.

Question 4

What plotting backends does Physt support and are they reliable?

Accepted Answer

Physt supports matplotlib for static plots, vega (broken), plotly (basic), folium for maps, and ASCII. Matplotlib is the most stable and feature-rich, while others are experimental, so users should verify compatibility for their specific visualization needs.

Question 5

How do I export Physt histograms to JSON or other formats?

Accepted Answer

Physt provides I/O capabilities for exporting to JSON, xarray, ROOT files, and pandas DataFrames. Use methods like `hist.to_json()` or similar conversions as mentioned in the features list, though some formats like xarray are experimental and may require additional dependencies.

Question 6

Is Physt suitable for big data with dask arrays?

Accepted Answer

Physt has alpha-level support for dask arrays, allowing memory-effective histogramming of large datasets, but this feature is not fully stable. For production big data workflows, consider testing thoroughly or using more mature alternatives like boost-histogram.

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